ABSTRACT
Background: As of April 2020, most of the confirmed cases outside Hubei province have been cured or confirmed dead in China. We aimed to understand environmental factors leading to COVID-19-related mortality in non-Hubei region. Methods: : We collected spatial-temporal and environmental data of 99 cases of COVID-19-related deaths outside of Hubei province in Mainland China between January 22, 2020 and April 6, 2020. A descriptive analysis, including a spatial-temporal distribution of daily reported diagnosed cases and related deaths, was conducted. We analyzed the possible environmental factors that affect the provincial-level case fatality rate (CFR) of COVID-19 outside Hubei, China. Results: Among the 99 reported deaths, 59 (59.6%) were male and 40 (40.4%) were female. The mean age at death was 71.30 (SD 12.98) years and 74 deaths were among those 65 years or older. The CFR was negatively correlated with temperature (r=-0.679, P <0.001) and humidity (r=-0.607, P =0.002), while latitude was positively correlated with the CFR (r=0.636, P =0.001). There were no statistically significant associations between CFR and the social environment factors. Conclusion: Higher CFR of COVID-19 was associated with lower temperature, lower humidity, and higher latitude. Continual analysis of daily reported diagnoses and mortality data can help healthcare professionals and policy makers understand the trends within a country in order to better prepare nationwide prevention and care guidelines, along with adequately appropriate funds accordingly.
Subject(s)
COVID-19ABSTRACT
Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020. Methods: Based on the publicly available surveillance data, we identified 21 transmission events, which occurred in Hong Kong, and had primary cases known, as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions, Gamma, Weibull and lognormal, that govern the SI of COVID-19. We selection the distribution according to the Akaike information criterion corrected for small sample size (AICc). Findings: We found the Lognormal distribution performed lightly better than the other two distributions in terms of the AICc. Assuming a Lognormal distribution model, we estimated the mean of SI at 4.9 days (95%CI: 3.6−6.2) and SD of SI at 4.4 days (95%CI: 2.9−8.3) by using the information of all 21 transmission events in Hong Kong. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak.
Subject(s)
COVID-19 , Coronavirus InfectionsABSTRACT
The novel coronavirus disease 2019 (COVID-19) outbreak and Italy has caused 6088 cases and 41 deaths in Republic of Korea and 3144 cases and 107 death in Italy by 5 March 2020. We modeled the transmission process in Republic of Korea and Italy with a stochastic model and estimated the basic reproduction number R0 as 2.6 (95% CI: 2.3-2.9) or 3.2 (95% CI: 2.9-3.5) in Republic of Korea, under the assumption that the exponential growth starting on 31 January or 5 February 2020, and 2.6 (95% CI: 2.3-2.9) or 3.3 (95% CI: 3.0-3.6) in Italy, under the assumption that the exponential growth starting on 5 February or 10 February 2020. Estimates of dispersion term (k) were 10 (95% CI: 5-56) or 22 (95% CI: 8-61) in Republic of Korea, and 13 (95% CI: 5-61) or 37 (95% CI: 13-61) in Italy, and all of which imply few super-spreading events.
Subject(s)
COVID-19 , DeathABSTRACT
As of 1 March 2020, Iran has reported 987 COVID-19 cases and including 54 associated deaths. At least six neighboring countries (Bahrain, Iraq, Kuwait, Oman, Afghanistan and Pakistan) have reported imported COVID-19 cases from Iran. We used air travel data and the cases from Iran to other Middle East countries and estimated 16533 (95% CI: 5925, 35538) COVID-19 cases in Iran by 25 February, before UAE and other Gulf Cooperation Council countries suspended inbound and outbound flights from Iran.
Subject(s)
COVID-19ABSTRACT
Backgrounds: In December 2019, a novel coronavirus (COVID-19) pneumonia hit Wuhan, Hubei Province, China and spread to the rest of China and overseas. The emergence of this virus coincided with the Spring Festival Travel Rush in China. It is possible to estimate total number of cases of COVID-19 in Wuhan, by 23 January 2020, given the cases reported in other cities and population flow data between cities. Methods: We built a model to estimate the total number of cases in Wuhan by 23 January 2020, based on the number of cases detected outside Wuhan city in China, with the assumption that if the same screening effort used in other cities applied in Wuhan. We employed population flow data from different sources between Wuhan and other cities/regions by 23 January 2020. The number of total cases was determined by the maximum log likelihood estimation. Findings: From overall cities/regions data, we predicted 1326 (95% CI: 1177, 1484), 1151 (95% CI: 1018, 1292) and 5277 (95% CI: 4732, 5859) as total cases in Wuhan by 23 January 2020, based on different source of data from Changjiang Daily newspaper, Tencent, and Baidu. From separate cities/regions data, we estimated 1059 (95% CI: 918, 1209), 5214 (95% CI: 4659, 5808) as total cases in Wuhan in Wuhan by 23 January 2020, based on different sources of population flow data from Tencent and Baidu. Conclusion: Sources of population follow data and methods impact the estimates of local cases in Wuhan before city lock down. Keyword: COVID-19; mobility; pneumonia; transportation; outbreaks
Subject(s)
COVID-19 , PneumoniaABSTRACT
The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess ship has caused over 634 cases as of February 20, 2020. We model the transmission process on the ship with a stochastic model and estimate the basic reproduction number at 2.2 (95%CI: 2.1-2.4). We estimate a large dispersion parameter than other coronaviruses, which implies that the virus is difficult to go extinction. The epidemic doubling time is at 4.6 days (95%CI: 3.0-9.3), and thus timely actions were crucial. The lesson learnt on the ship is generally applicable in other settings.
Subject(s)
COVID-19ABSTRACT
Backgrounds: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. Based on the publicly available surveillance data, we identified 21 transmission chains in Hong Kong and estimated the serial interval (SI) of COVID-19. Methods: Index cases were identified and reported after symptoms onset, and contact tracing was conducted to collect the data of the associated secondary cases. An interval censored likelihood framework is adopted to fit a Gamma distribution function to govern the SI of COVID-19. Findings: Assuming a Gamma distributed model, we estimated the mean of SI at 4.4 days (95%CI: 2.9-6.7) and SD of SI at 3.0 days (95%CI: 1.8-5.8) by using the information of all 21 transmission chains in Hong Kong. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are recommended in combating the COVID-19 outbreak.
Subject(s)
COVID-19 , Coronavirus InfectionsABSTRACT
Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China in the end of 2019, and soon spread oversea. A comprehensive and timely review summarized the scientific research in estimating the basic reproduction number (R0) released from 1 January to 7 February 2020 [1]. During the early outbreak, when the key epidemiological features of COVID-19 were uncovered, the R0 estimation largely relied on the growth rate of the epidemic curve and the estimation of the serial interval (SI). Here, we demonstrated that an overlarge SI would lead to overestimation of R0. We adopted the growing process proposed in [2] deterministically with a population of 11 million in Wuhan, 1 case initially onset on 5 December 2019 and a fixed step at 1 day. We consider two values of the mean serial interval (SI) that are SI at 4.6 days estimated based on 28 records of transmission chains [3], which was largely consistent with the SI estimate at 4.4 days based on 71 records [4];and SI at 8 days, which was closer to the SI of the severe acute respiratory syndrome (SARS, 8.4 days), SI of the Middle East respiratory syndrome (MERS, 7.6 days). As for demonstration that a larger SI could lead to overestimation in R0, we conducted the simulation with two schemes that are Scheme (I): R0 = 2, and SI = 4.6 days;and Scheme (II): R0 = 2, 3, 4 and 3.3 as summarized in [1], and SI = 8 days. We also compared the simulation results with the previous estimates of the cumulative number of COVID-19 infection in Wuhan. In Fig 1, the simulation results of the scheme (I) had almost the same growing trends as those of scheme (II) with R0 = 3.3. Although a higher R0 could force the epidemic curve increasing rapidly, a shorter SI could increase iteration of transmission generation, i.e., transmission may occur shortly post infection. According to the simple approximated formula that R0 = exp(γ∙SI), where γ was the exponential growth rate calculated from the incidence data directly, a longer SI would lead to a higher R0 estimate theoretically. With a shorter SI at 4.6 days, which was supported by richer datasets in [3, 4], the R0 of COVID-19 could be lower than previous estimates based on longer SI. By using the growth rate (γ) at 0.15 per day, the R0 was found at 2.0 with SI at 4.6 days, whereas 3.3 with SI at 8 days. Although the effects of public health control were ignored in this analysis, our model could be Downloaded from https://academic.oup.com/jtm/advance-article-abstract/doi/10.1093/jtm/taaa033/5803291 by guest on 13 March 2020 4 extended by introducing an effective reproduction number accounting for the effectiveness of the control measures, and we remarked this modification would not affect the main conclusion. Furthermore, as pointed out in [3], provided that the SI of COVID-19 might be shorter than its incubation period, pre-symptomatic transmission may occur shortly after being infected [5]. This implies that a fraction of transmissions cannot be prevented solely through isolating the symptomatic cases, since the time when contact tracing is conducted, they may have already been infectious and generated secondary cases. Therefore, the effectively quarantine of suspected (and probable) cases, as well as close contacts, and timely contact tracing were crucial in successful outbreak mitigation.